A/B Testing using Python

Data Preparation

I will begin by examining the relationship between the number of impressions received from both campaigns and the respective amounts spent on those campaigns.

The control campaign yielded a higher number of impressions relative to the amount spent on both campaigns. Now, let's shift our focus to the number of searches conducted on the website from both campaigns.

The test campaign led to a higher volume of searches on the website. Now, let's turn our attention to the number of website clicks from both campaigns.

The test campaign emerges as the winner in terms of the number of website clicks. Now, let's proceed to examine the amount of content viewed after visitors reached the website from both campaigns.

The audience of the control campaign observed a greater amount of content compared to the test campaign. Although the difference is not substantial, considering the relatively low website clicks in the control campaign, its engagement on the website surpasses that of the test campaign. Now, let's shift our focus to the number of products added to the cart from both campaigns.

Despite having fewer website clicks, the control campaign managed to accumulate more products added to the cart. Now, let's examine the amount spent on both campaigns.

The amount spent on the test campaign surpasses that of the control campaign. However, considering that the control campaign generated more content views and more products in the cart, it appears that the control campaign is more efficient than the test campaign. Now, let's proceed to examine the purchases made by both campaigns.

There's only a marginal difference of approximately 1% in the purchases made from both ad campaigns. Since the control campaign achieved more sales with a lower marketing expenditure, the control campaign emerges as the winner in this aspect.

Let's delve into the analysis of some key metrics to determine which ad campaign converts more effectively. We'll begin by examining the relationship between the number of website clicks and content viewed from both campaigns.

The test campaign records higher website clicks, but the engagement derived from these clicks is higher in the control campaign. Therefore, the control campaign secures a victory in this aspect.

Now, let's proceed with the analysis of the relationship between the amount of content viewed and the number of products added to the cart from both campaigns.

Once again, the control campaign emerges as the winner! Now, let's shift our focus to the relationship between the number of products added to the cart and the number of sales from both campaigns.

Despite the control campaign generating more sales and having a higher number of products in the cart, it's worth noting that the conversion rate of the test campaign is higher.

Conclusion

From the above A/B tests, it's evident that the control campaign achieved more sales and higher visitor engagement. The control campaign received greater attention, with more products viewed, leading to a higher number of products in the cart and ultimately more sales. However, it's essential to note that the conversion rate of products in the cart is higher in the test campaign. The test campaign succeeded in generating more sales in relation to the products viewed and added to the cart. In summary, the test campaign can be effectively used to market a specific product to a targeted audience, while the control campaign is well-suited for promoting multiple products to a broader audience.